Issues of Validity and Logistic Regression: Statistical Models for Predicting Multipoint Competition in the U.S. Airline Industry

1997 ◽  
pp. 133-157
Author(s):  
Faye I. Smith
Keyword(s):  
Logistic Regression ◽  
Airline Industry ◽  
Statistical Models ◽  
The U.S

DECISION MAKING BY VARIABLE CONTRIBUTION IN DISCRIMINANT, LOGIT, AND REGRESSION ANALYSES

2004 ◽  
Vol 03 (02) ◽  
pp. 265-279 ◽  
Author(s):  
STAN LIPOVETSKY ◽  
MICHAEL CONKLIN
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Statistical Models ◽  
Regression Analyses ◽  
Discriminant Functions ◽  
Multivariate Statistical ◽  
Linear Discriminant ◽  
Variable Contribution ◽  
Linear Aggregate

Comparative contribution of predictors in multivariate statistical models is widely used for decision making on the importance of the variables for the aims of analysis and prediction. However, the analysis can be made difficult because of the predictors' multicollinearity that distorts estimates for coefficients in the linear aggregate. To solve the problem of the robust evaluation of the predictors' contribution, we apply the Shapley Value regression analysis that provides consistent results in the presence of multicollinearity both for regression and discriminant functions. We also show how the linear discriminant function can be constructed as a multiple regression, and how the logistic regression can be approximated by linear regression that helps to obtain the variables contribution in the linear aggregate.

ASYMMETRIES IN UNIVERSAL GRAMMAR The Role of Method and Statistics

2000 ◽  
Vol 22 (2) ◽  
pp. 209-228 ◽  
Author(s):  
John C. Paolillo
Keyword(s):  
Logistic Regression ◽  
Statistical Analysis ◽  
Statistical Models ◽  
Logistic Regression Model ◽  
Universal Grammar ◽  
Nonnative Speakers ◽  
Potential Explanation ◽  
Differential Learning ◽  
Grammaticality Judgments

Felix (1988) claimed to demonstrate that UG-based knowledge of grammaticality causes nonnative speakers (NNSs) to have more accurate grammaticality judgments on sentences that are ungrammatical according to UG than on those that are grammatical. Birdsong (1994) criticized the methodology employed, noting that it ignores “response bias” (a propensity to judge sentences as ungrammatical) as a potential explanation. Felix and Zobl (1994) dismissed this criticism as merely methodological. In this paper, Birdsong's criticism is upheld by considering a statistical model of the data. At the same time, a more complete logistic regression model allows a fuller statistical analysis, revealing tentative support for the asymmetry claim, as well as differential learning states for different constructions and a tendency toward transfer avoidance. These theoretically significant effects were unnoticed in the earlier discussion of this research. For SLA research on grammaticality judgments to proceed fruitfully, appropriate statistical models need to be considered in designing the research.

scholarly journals Best Paper Selection

2017 ◽  
Vol 26 (01) ◽  
pp. 212-213
Keyword(s):  
Logistic Regression ◽  
Private Information ◽  
Statistical Models ◽  
Heart Sound ◽  
Information Leakage ◽  
Scientific Data ◽  
Training Data ◽  
Genotype Data ◽  
Scientific Data Management ◽  
Heart Sound Segmentation

Agarwal V, Podchiyska T, Banda JM, Goel V, Leung TI, Minty EP, Sweeney TE, Gyang E, Shah NH. Learning statistical models of phenotypes using noisy labeled training data. J Am Med Inform Assoc 2016;23(6):1166-73 https://academic.oup.com/jamia/article-lookup/doi/10.1093/jamia/ocw028 Harmanci A, Gerstein M. Quantification of private information leakage from phenotype-genotype data: linking attacks. Nat Methods 2016;13(3):251-6 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4834871/ Pfiffner PB, Pinyol I, Natter MD, Mandl KD. C3-PRO: Connecting ResearchKit to the Health System Using i2b2 and FHIR. PloS One 2016;11(3):e0152722 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4816293/ Wilkinson MD, Dumontier M, Aalbersberg IJJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten JW, da Silva Santos LB, Bourne PE, Bouwman J, Brookes AJ, Clark T, Crosas M, Dillo I, Dumon O, Edmunds S, Evelo CT, Finkers R, Gonzalez-Beltran A, Gray AJ, Groth P, Goble C, Grethe JS, Heringa J, ‘t Hoen PA, Hooft R, Kuhn T, Kok R, Kok J, Lusher SJ, Martone ME, Mons A, Packer AL, Persson B, Rocca-Serra P, Roos M, van Schaik R, Sansone SA, Schultes E, Sengstag T, Slater T, Strawn G, Swertz MA, Thompson M, van der Lei J, van Mulligen E, Velterop J, Waagmeester A, Wittenburg P, Wolstencroft K, Zhao J, Mons B. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 2016;3:160018 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4792175/ Springer DB, Tarassenko L, Clifford GD. Logistic regression-HSMM-based heart sound segmentation. IEEE Trans Biomed Eng 2016 Apr;63(4):822-32

scholarly journals Assessing the Influence of Living and Working Conditions on Alcohol Consumption in Migrant Farmworkers in Mexico

2016 ◽  
Vol 14 (3) ◽  
pp. 12-21
Author(s):  
Luis A. Valdez ◽  
Melanie L. Bell ◽  
David O. Garcia
Keyword(s):  
Logistic Regression ◽  
Alcohol Consumption ◽  
Alcohol Use ◽  
Working Conditions ◽  
Ordinal Logistic Regression ◽  
Consumption Patterns ◽  
Migrant Farmworkers ◽  
Survey Results ◽  
Working And Living Conditions ◽  
The U.S

Background and Purpose: Inadequate working and living conditions are associated with alcohol consumption in farmworkers in the U.S. However, the influence of these factors on alcohol consumption patterns in migrant farmworkers in Mexico remains unclear. The purpose of this analysis was to assess the influence of housing and working conditions on alcohol use in migrant farmworkers in Mexico. Methods: We used logistic and ordinal logistic regression to examine the association of living and working conditions on alcohol consumption and frequency in 3,132 farmworkers in Mexico with data from a Mexican national farmworker’s survey. Results: Living in inadequately built homes (OR=0.84; 95% CI=0.72, 0.98; p

scholarly journals Using the Econometric Models for Identification of Risk Factors for Albanian Smes (Case Study: Smes of Gjirokastra Region)

2021 ◽  
Vol 18 ◽  
pp. 163-170
Author(s):  
Lorenc Koçiu ◽  
Kledian Kodra
Keyword(s):  
Logistic Regression ◽  
Statistical Models ◽  
Logistic Regression Model ◽  
Qualitative Data ◽  
Linear Models ◽  
Binary Logistic Regression ◽  
Random Variable ◽  
Econometric Models ◽  
Binary Logistic Regression Model

Using the econometric models, this paper addresses the ability of Albanian Small and Medium-sizedEnterprises (SMEs) to identify the risks they face. To write this paper, we studied SMEs operating in theGjirokastra region. First, qualitative data gathered through a questionnaire was used. Next, the 5-level Likertscale was used to measure it. Finally, the data was processed through statistical software SPSS version 21,using the binary logistic regression model, which reveals the probability of occurrence of an event when allindependent variables are included. Logistic regression is an integral part of a category of statistical models,which are called General Linear Models. Logistic regression is used to analyze problems in which one or moreindependent variables interfere, which influences the dichotomous dependent variable. In such cases, the latteris seen as the random variable and is dependent on them. To evaluate whether Albanian SMEs can identifyrisks, we analyzed the factors that SMEs perceive as directly affecting the risks they face. At the end of thepaper, we conclude that Albanian SMEs can identify risk

The Geography of Deregulation in the U.S. Airline Industry

2017 ◽  
pp. 199-224
Author(s):  
Andrew R. Goetz * ◽  
Christopher ] Sutton **
Keyword(s):  
Airline Industry ◽  
The U.S
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